Parameter Optimization of Adaptive Flux-Weakening Strategy for Permanent-Magnet Synchronous Motor Drives Based on Particle Swarm Algorithm

Abstract

Operating in the high-speed range is necessary for high-performance permanent-magnet synchronous motor (PMSM) drives. However, due to the back electromotive force effect, the PMSM is approaching the voltage limit at field decreasing scope. This paper presents a new flux-weakening scheme along with an improved vector control strategy to alleviate the influence of this problem. Control parameters of the antiwindup proportional and integral (AWPI) controller are optimized off-line in relying on an adaptive velocity particle swarm optimization (AVPSO) algorithm. The AVPSO algorithm considers the summation of AWPI measurement error which is the objective function of the optimization problem without knowing the transfer function of the plant. Hence, the tuned flux-weakening controller with a filter is used to set the flux level without saturating the current controllers. Meanwhile, the other controllers of inner and outer loops award a great dynamic and steady-state performance for the PMSM. In the proposed scheme, the flux-weakening control is not dependent on machine parameters that adapts the flux level automatically and provide a fast transition between the constant torque region and the field-weakening region. Effectiveness and advantages of the proposed scheme are presented in this paper through both simulation and experimental results.

Speed sensorless direct voltage control of brushless doubly-fed machine independent generation system based on improved particle swarm optimization algorithm

“…Although the controller parameters optimized by the two PSO algorithms of literature [6] and literature [7] can basically meet the requirements, they still have defects compared with the method proposed in this paper. Literature [6] and literature [7] have obvious voltage retraction and fluctuation during the rising process, while the method proposed in this paper rise more smoothly and it has less fluctuation and better anti-interference ability than other methods when speed changed at 3 s and the voltage obtained by it has less change and can reach stability faster when the load changes at 3.5s, which achieves the requirement of accurate, fast and stable control system with higher control quality. The results of the performance index function obtained by the algorithm of this study are the best, and the control parameters thus determined are optimal.…”

“…Table1. IPSO parameter optimization To further demonstrate the effectiveness of this improved PSO parameter optimization algorithm, the improved method of this study is compared with the improved PSO methods of the literature [6] and literature [7]. The initial population is 30, the dimension of the variables is 8, and the range of the optimization-seeking variables is [-50 50] The number of iterations is 200.…”

Multiobjective Optimization of Roadheader Shovel-Plate Parameters Using Gray Weight and Particle Swarm Optimization

“…, 𝑘). Thus, any form of multiobjective optimization problem can be transformed into a unified expression as follows [27]:…”

A Review about Flux-Weakening Operating Limits and Control Techniques for Synchronous Motor Drives

“…Particular attention needs to be paid to avoid the wind-up phenomenon in the voltage loop. Thus, a back-calculation anti-windup strategy is proposed in [55]. As a further relevant contribution, the same paper proposes an adaptive velocity particle swarm optimization algorithm to optimize the control parameters of the anti-windup proportional and integral controller.…”